evalml.model_understanding.roc_curve

evalml.model_understanding.roc_curve(y_true, y_pred_proba)

Given labels and classifier predicted probabilities, compute and return the data representing a Receiver Operating Characteristic (ROC) curve. Works with binary or multiclass problems.

Parameters

• y_true (ww.DataColumn, pd.Series or np.ndarray) – True labels.

• y_pred_proba (ww.DataColumn, pd.Series or np.ndarray) – Predictions from a classifier, before thresholding has been applied.

Returns

A list of dictionaries (with one for each class) is returned. Binary classification problems return a list with one dictionary.

Each dictionary contains metrics used to generate an ROC plot with the following keys:

• fpr_rate: False positive rate.

• tpr_rate: True positive rate.

• threshold: Threshold values used to produce each pair of true/false positive rates.

• auc_score: The area under the ROC curve.

Return type

list(dict)

evalml.model_understanding.precision_recall_curve evalml.model_understanding.calculate_permutation_importance